Data on spatial and temporal modelling of soil water storage in the Guinea savannah zone of Northern Ghana

In this article, we present the space-time variability of soil moisture (SM) and soil water storage (SWS) from key agricultural benchmark soil types measured across the Guinea savannah zone of Ghana (n ≈ 2,000 measurements) in a single cropping season (Nketia et al., 2022). From 36 locations, SM measurements were obtained with a PR2/60 moisture probe calibrated for a 0–100 cm soil depth interval (at six depths). We further introduce a new pedotransfer model that was developed in deriving the SWS for the same depth interval of 0–100 cm. Assessing information on the space-time variability of SM and SWS is essential for agricultural intensification efforts, especially in semi-arid landscapes of sub-Saharan Africa (SSA), where there is the need and the potential to increase food-crop production. This dataset spans the main topographic units of the Guinea savannah zone and covers dominant vegetation types and land uses of the region, which is similar to most parts of West Africa. The comprehensive dataset and the customized machine learning models can be used to support crop production with respect to water management and optimized agricultural resource allocation in the Guinea savannah landscapes of Ghana and other parts of SSA.


Value of the Data
• The data provides information on space-time SM and SWS over 0-100 cm soil depth for key agricultural benchmark soils of the Guinea savannah zone of Ghana. • It provides useable information on the 4D SWS distribution of the Guinea savannah region of Ghana, which can support farmers in estimating where, when, how much, and for how long SWS is available for cultivation [1] . • The data is useful for soil and agronomic research into crop yield production limited by water stress, such as modelling scenarios of water management for dry-season farming.

Data Description
The data presented in this paper illustrates the space-time variability of soil moisture (SM) and soil water storage (SWS) of 36 stratified locations of the Guinea savannah zone of Ghana ( n ≈ 20 0 0). Fig. 1 shows the study area and locations from where in situ SM measurement were collected, covering a 170 × 190 km area across seven key agricultural benchmark soil types.  Table 1 shows site characteristics and their associated GPS coordinates for the 36 measurement locations. Fig. 2 illustrates how the in situ SM measurements and soil sampling at each measurement location was conducted. We modelled vertical variation in SWS for the 36 locations, using a set of pedotransfer algorithms, converting the in situ measured SM at standard depths (i.e., 10, 20, 30, 40, 60, and 100 cm) into six depth intervals (i.e., 0-5, 5-15, 15-30, 30-40, 40-60, and 60-100 cm) as per GlobalSoilMap specifications [2] . The data file called 'Code_C1.R' ( https://zenodo.org/record/6447871#.YlQpc8hByHs ) shows the fully commented systematic SWS modelling framework used in deriving SWS data within this article. Fig. 3 depicts a soil catena showing the soil types of the study area along which measurements were undertaken. The dataset can also be grouped based on the seven key benchmark soil types covering the three topographical units of the study area ( Fig. 3 ) [4] . The upper slope is covered by Eutric Plinthosols (Kpelesawgu series in the local Ghanaian soil classification system). Soils on middle to lower slopes include Gleyic Planosols (Lima series), Petric Plinthosols (Changnalili series) and Chromic Lixisols (Kumayili series), and soils on toe slopes are Gleyic Fluvisols (Dagare series), Plinthic Lixisols (Siare series) and Fluvic Gleysols (Volta series).
The shared dataset reported in the article is stored in an excel file, called 'File_T1_SM_SWS.xlsx' ( https://zenodo.org/record/6447871#.YlQpc8hByHs ). The 'File_T1_SM_SWS.xlsx' file contains two spreadsheets, i.e . , 'SM' for raw SM data, and 'SWS' for the calculated SWS data. The variables in each data sheet are specified below: • Sheet 'SM' shows the station IDs of the in situ measurement locations (column 1) and the lower soil depths (in cm) at which SM measurements were taken (column 2). Columns 3 and 4 contain the raw volumetric SM measurements expressed in percentages and their associated measurement dates, respectively. Columns 5 and 6 show WGS84 coordinates of the measurement stations in latitude and longitude, respectively.  • In sheet 'SWS', also station IDs of the measurement locations (column 1) and their corresponding dates of measurement (column 2) are presented. Columns 3 and 4 contains the upper and lower soil depth, respectively, (both in cm), for which SM measurements were taken. Columns 5 and 6 contains the benchmark soil types (in the Ghanaian local system) and their equivalent FAO World Reference Base classification, respectively. Columns 7 and 8 show soil thickness (in cm) and its corresponding calculated SWS (expressed by an absolute value in mm), respectively. Column 9 shows the topographic units along which the seven key benchmark soil types occur.

In situ SM measurements
The 36 measurement locations were stratified following an unbiased approach that coupled the global weighted principal component algorithm with a cost-constrained conditioned Latin hypercube algorithm [3] . With this approach, it was possible to account for the maximum local spatial structures of the study area, while selecting optimized locations that highly influenced SM variability.
SM measurements were taken in 36 soil profiles, located on the three main soil topographic units: upper, middle-lower, and toe slopes ( Fig. 3 ). At each location, an access tube was installed ( Fig. 2 A-C), where SM was measured at six standard depths within the 0-100 cm depth (i.e., 10, 20, 30, 40, 60, and 100 cm) using a calibrated moisture probe (PR2/60, Delta-T Devices) ( Fig. 2 D). One of the objectives of work reported in the associated paper of this data article, Nketia et al. [1] was to estimate SM from Sentinel-1 data. Thus, the SM measurements were timed to coincide with the overpass of the Sentinel-1 satellite at a temporal resolution of 12 days for ten time-steps covering the whole dry season (i.e., February-June). Thus, in total 2,160 soil measurements were taken.

SWS modelling framework
An important contribution of this data is the modelled SWS. This part of the data was derived by implementing a pedotransfer algorithm in two main stages as illustrated in Fig. 4 . In a first step, in situ SM measurements were vertically discretized into six depth intervals (i.e., 0-5, 5-15, 15-30, 30-40, 40-60, and 60-100 cm) following the GlobalSoilMap specifications [2] . In a second step, SWS at each data point was recursively profiled as a function of the measured in situ SM, bulk density and the effective soil thickness between two soil layers [1] . By this approach, we accounted for the differential availability of SWS critical to the management of shallow and deep-rooted plants notable to the study area. This approach also allowed us to account for the effect of soil depth on in situ SM measurements.
The study area is characterized by an inherent plinthic and petro-plinthic horizon, occurring at 70-100 cm depth [4] and thus restricting water movements between lower and upper soil layers. Because of this situation groundwater movement was not considered in the SWS modelling framework. Thus, we only assumed SWS for the succeeding soil depth (d) as a reservoir for the preceding soil depth ( d − 1 ) at a time-step (t) . For this rationale, observed changes in measured SM of the soil depths is proportional to the change in modelled SWS at a location between a preceding and a succeeding soil depth at a time-step. Eq. (1) defines the SWS model, which is expressed by an absolute value in mm. For each in situ SM measurement at each point in time and soil depth, we accounted for the SWS loss or gain at this point with respect to its initial state [ 5 , 6 ]. Well annotated R [7] scripts that were used in modelling SWS are presented in the file 'Code_C1.R' (available at https://zenodo.org/record/6447871#.YlQpc8hByHs ).
where input parameters for function ( f ) , calculated at a constant factor of 0.1 (from density of water of 1 g cm -3 ), were in situ SM ( S M itd ; % V ol ) at location (i ), time-step (t) and soil depth (d) , bulk density laboratory data (B D id ; g c m −3 ) and respective soil thickness (h id ; cm ) . R t explains the rate of loss or gain in S M itd between a preceding and subsequent soil depth interval [6] , and varies from 0 (low loss or gain) to ± 1 (high loss or gain). Fig. 5 illustrates the variability of SWS per each benchmark soil type along the in situ measurement depth intervals.

Ethics Statement
There is no conflict of interest. The data is available to the general public.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability
Data on space-time soil moisture and modelled soil water storage, Ghana (Original data) (Zenodo).